Experimental Aging Research, 41: 240–258, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0361-073X print/1096-4657 online DOI: 10.1080/0361073X.2015.1021641

TRAJECTORIES OF DISABILITY AND THEIR RELATIONSHIP WITH HEALTH STATUS AND SOCIAL SERVICE USE Chun-Min Chen Department of Health Care Management, University of Kang Ning, Tainan, Taiwan

Yung-Yu Su Graduate Institute of Health Care, Meiho University, Ping-Tung, Taiwan

Judy Mullan School of Medicine, University of Wollongong, New South Wales, Wollongong, Australia

Ming-Shyan Huang Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

Received 1 April 2013; accepted February 2014. Herng-Chia Chiu is also Adjunct Professor at the Department of Business Administration, School of Business Administration, Sun Yat-sen University, Taiwan. Address correspondence to Herng-Chia Chiu, PhD, Professor, Department of Business Administration, School of Business Administration, Sun Yat-sen University, Taiwan. E-mail: [email protected]

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241 Herng-Chia Chiu

Department of Healthcare Administration and Health Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan Background/Study Context: This longitudinal study was conducted between 1994 and 2004 in a cohort of southern Taiwan community-living older residents. The study aims to explore the trajectories of disability and how these patterns differed between respondents who survived and those who died during data collection phases; this study also investigated how health status change and social service use predicted the different trajectories of disability. Methods: Disability, chronic disease, depression, and social service usage data were collected over six waves. Clusters of disability were used to define a categorical response variable. Baseline levels and new occurrences of chronic disease and depression and the frequency of social service use during this period were chosen as the predictors of disability trajectories. Results: Changes in levels of disability during the aging process were identified. Different trajectories clearly reflected heterogeneity within disability clusters and between surviving and nonsurviving respondents. This study highlighted that hypertension and depression were predictors of increased disability among both surviving and nonsurviving respondents, whereas diabetes was only found to be a strong predictor of increased disability for the nonsurviving respondents. In addition, this study found that use of social services such as personal care, homemaker-household, and physical therapy were significantly associated with an increase in disability, whereas use of recreational services seemed to be associated with a decrease in disability. Conclusions: These findings identify disability to be a highly dynamic process, which can be characterized into different trajectory clusters (e.g., no, mild, and major disability clusters). A greater awareness of these trajectories could be used to better target strategies to prevent and/or manage disabilities in an aging population.

Disability in older people is a dynamic process (Verbrugge & Jette, 1994), which can follow many different patterns (Gill, Guo, & Allore, 2008; Hardy, Dubin, Holford, & Gill, 2005; Peres, Verret, Alioum, & BarbergerGateau, 2005). The disability process can therefore be conceptualized as a series of transitions between different levels of disability and independence in undertaking activities of daily living. However, only a few studies have considered the trajectories of disability or have attempted to understand various types of disability that go further than the dichotomous state of having a disability versus not having a disability (Barberger-Gateau,

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Rainville, Letenneur, & Dartigues, 2000; Romoren & Blekeseaune, 2003). Moreover, most studies have documented transitions in disability that have followed nearly every conceivable pattern of functional change (Deeg, 2005; Romoren & Blekeseaune, 2003). However, these studies have been limited by small sample sizes and short intervals between the assessments of disability. In addition, exclusions due to death and loss to follow-up may further limit the ability to capture all possible courses of disability affecting older people over time (Gill et al., 2008). The literature on disability trajectories suggests that studying disability as a process over time is appropriate and very informative with regard to the disability experience of an aging population. The evidence also suggests that clusters of disability trajectories should be examined because studying only one trajectory of disability at a time, without considering the end-stage disability status, does not sufficiently explain the disability process (Deeg, 2005; Gill et al., 2008). Based on current longitudinal evidence, factors that increase the risk of functional decline include sociodemographics (Nusselder, Looman, & Mackenbach, 2006; Peres et al., 2005), age-related chronic diseases (Nusselder et al., 2006; Peres et al., 2005), and depression (Mehta, Yaffe, & Covinsky, 2002; Peres et al., 2005). There is little evidence about the functional changes that occur in older participants who survive or cease to survive during longitudinal studies. Furthermore, few studies have attempted to link changes in disability status with both existing and new occurrences of chronic disease(s) and depression. Therefore, the intention of this study was to elucidate the chronological changes associated with disability over a 10-year period in the older participants, and to identify possible associations between disability with chronic disease(s) and depression. An association between social service use and health status has long been noted by researchers (McColl, 2005; Wan, 1987). Previous studies have focused more on how disabilities have affected social service use and agree that disabilities are the primary predictors of service usage (Broe et al., 2002; McColl, 2005). It is unclear, however, whether an increase in social service use can improve or reduce the onset of disability among older people (Maier & Klumb, 2006; Penning, 2002). A better understanding of such issues could be used to help delay health decline and improve health outcomes for older people through the provision of more targeted and appropriate health care social services. The aim of this study was to explore trajectories of disability over time, the determinants of these trajectories, and their relationship with social service use. We first hypothesized that older people experience an increasing trend of disabilities as they age. To better identify risk factors that impacted on this increasing trend, we hypothesized that poor health status was the

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strongest risk factor affecting the disability trajectory. To help predict the association between social service use and the possibility of becoming disabled, we hypothesized that some social service use was better than none in delaying the onset of disability. METHODS Study Population and Sample A longitudinal study of community-dwelling older people (aged 65 and over) living in the San-Min District, Kaohsiung City, Taiwan, was commenced in 1994 and involved five biennial follow-up surveys conducted over the following 10 years. This study adopted a prospective study design using a closed cohort. Baseline data on the entire older population were obtained from the Kaohsiung City government. In 1994, about 86,000 older adults lived in Kaohsiung City, which is the second largest metropolitan area in southern Taiwan, accounting for 8% of the 1.3 million people living in Taiwan. Among the 11 urban administration districts in Kaohsiung City, the SanMin District was selected for the study because the proportion of older people residing in the district was very similar to that in Kaohsiung City. Two-stage sampling was undertaken and involved a random selection of 21 basic administrative units of the San-Min District, as well as the random selection of 50% of the older people residing in these units to take part in face-to-face interviews. The two-stage sampling procedure identified 1436 eligible older people, 1260 of whom were interviewed at baseline (response rate 88%). Of the original 1260 respondents who were interviewed at baseline, 810 were selected for data analysis in this study because they had completed all six waves of the longitudinal survey responses or they had completed all survey wave responses prior to their death. Thus, the responding sample sizes analyzed for this study were 810 (1994), 721 (1996), 653 (1998), 571 (2000), 509 (2002), and 442 (2004). One respondent who died and was lost to follow-up after the first wave was excluded from the analysis because of missing baseline health status information, and a further 449 respondents (of the original 1260 older respondents) were excluded from the analysis because they were lost to follow-up and alive during one of the survey data collection waves. Notably, these 450 respondents who were excluded from the study had similar demographic characteristics, health status (chronic disease, disability, and depression) and social service use to the 810 respondents who were included in the study.

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Data and Measurement This study was based on data collected using the Chinese version of the Multidimensional Functional Assessment Questionnaire (CMFAQ); the reliability and validity of which has been investigated and found satisfactory (Chiu et al., 1997). The variables of interest for disability (response variable), chronic disease (predictor variable), depression (predictor variable), social service use (predictor variable), and demographic factors (control variables) were as follows. Response Variables Disability was assessed using the Instrumental Activities of Daily Living (IADL) (Lawton & Brody, 1969) and Physical Activities of Daily Living (PADL) (Katz, Ford, & Moskowitz, 1963) scales. The status for each of the seven IADL disabilities (phoning; using public transport; shopping; cooking; doing housework; taking medication; handling finances) and seven ADL disabilities (eating; dressing; grooming; walking; transferring; bathing; toileting) was measured during each of the data collection waves. Individual IADL and ADL disability scores ranged from 0 to 2, where a score of 0 points was awarded for the answer “no difficulty,” 1 point was awarded for “need some help,” and 2 points were awarded for the answer “completely impossible.” Each respondent was further classified as having no disability (score: 0) or a disability (score: 1–28) for IADL and ADL disabilities. The disability domain was further defined as no disability (able to perform all ADL and IADL activities), mild disability (unable to perform 1 or more IADLs but had no ADL disabilities), moderate disability (unable to perform 1–2 ADL activities), or severe disability (unable to perform 3 or more ADL activities). To explore patterns of functional change, trajectories of disability were identified according to the coded pathways. To capture all substantial changes in each individual, the functional status at each wave was coded as a pathway based on the disability domain. For alive and dead respondents, the pathways were further grouped into five clusters. Surviving respondents were classified into three trajectory clusters: ●



Trajectory Cluster 1 (T1)—no disability, alive: Included surviving respondents who reported having no disability during all six waves. Trajectory Cluster 2 (T2)—mild disability, alive: Included respondents who reported having a mild disability one or more times during the six waves or moderate/severe disability only once during all six waves.

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Trajectory Cluster 3 (T3)—major disability, alive: Included respondents who reported having a moderate/severe disability two or more times during all six waves.

In all cases of ambiguity, for example, when a respondent had indicated two instances of mild disability and two of moderate/severe disability, the respondent was classified in the trajectory cluster with the higher number, which in this example would have been T3. Nonsurviving respondents were classified into two trajectory clusters: ●



Trajectory Cluster 4 (T4)—mild disability, dead: Included nonsurviving respondents who reported having a mild disability during the six data collection waves. For respondents who died without a previous disability recorded, it was assumed that death was the onset of a disability that in such cases was recorded in the mild disability, dead cluster. Trajectory Cluster 5 (T5)—major disability, dead: Included nonsurviving respondents who reported having a moderate and/or severe disability during the six waves.

Predictor Variables Chronic disease morbidity data were obtained during each survey wave by asking respondents to self-report about whether a physician had ever told them that they had one or more of the following chronic diseases: arthritis (the most important disability-related chronic disease); diabetes (the most costly chronic disease); gastrointestinal problems (a rapidly increasing chronic disease); heart disease (the most life-threatening chronic disease); and/or hypertension (a chronic disease and a risk factor for other chronic diseases). The response for each chronic disease was reported as either “yes = 1” or “no = 0.” Depression was evaluated using the self-reported Short Psychiatric Evaluation Schedule (SPES) (Pfeiffer, 1979). The SPES is a 15-item questionnaire to which participants responded by indicating “yes = 1” or “no = 0” to questions about depressive symptoms. For the purpose of this study, a respondent was then classified as having no depression (score: 0–3) or depression (score: 4–15). Chronic disease and depression status were analyzed as time-varying variables because baseline and new occurrences status were included in the analysis. For example, respondents who had been diagnosed with arthritis by a physician at baseline were given a score of 1 and defined as previous disease, whereas if they suggested that they had a diagnosis of arthritis

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during any of the following survey data collection waves, they were give a score of 1 and defined as new occurrence disease. This same approach was used when analyzing the depression status of the respondents at baseline and for the consecutive data collection waves. The CMFAQ contains questions about the use of 10 social services (Fillenbaum, 1988), only 6 of which were selected for analysis in this study because they are home-based services and more likely to be used by older people with disabilities (Liu, Manton, & Aragon, 2000). These six social services included recreational services, homemaker-household services, meal preparation services, personal care services, nursing care services, and physical therapy services. Service use was analyzed as a time-varying variable, each of the six selected services was categorized at each survey wave (1994–2004) by a dichotomous variable: 1 = use, 0 = do not use. Control Variables The baseline demographic factors were analyzed as control variables, including age (65–69 years, 70–74 years, 75 years and over); gender; and educational level achieved (illiterate, 1–6 years, 7 or more years of education). Statistical Analyses Multinomial regression analysis was adopted to model the factors predicting different trajectories for disability over the six waves. Instead of standard logistic regression, multinomial regression was used when the response variables had more than two outcome possibilities. In an analogous manner to logistic regression, multinomial regression allows correcting for confounders and it expresses the results using odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS Clustered Trajectories of Disability Table 1 presents the baseline characteristics (e.g., demographic information, chronic disease, depression, and social service use status) of the 810 respondents who have been allocated into their five different disability trajectory clusters. In four of the disability trajectory clusters (T1, T2, T3, and T4) the majority of the respondents were in the younger 65–69 years age group, whereas in the T5 disability trajectory cluster the majority of the respondents were older in the 75+ years age group. The most highly

Note. T = Trajectory.

Age group 65–69 years 70–74 years 75+ years Gender Female Male Education None 1–6 years 7+ years Chronic disease None 1 chronic disease 2+ chronic diseases Depression No depressive symptoms Depressed Social service use Do not use Use

Characteristic

72.3% 22.8% 5.0% 35.6% 64.4% 7.9% 45.5% 46.5% 30.7% 40.6% 28.7% 88.1% 11.9% 0.0% 100.0%

36 65

8 46 47

31 41 29

89 12

0 101

%

73 23 5

n

T1 No disability, alive (n = 101)

1 229

186 44

64 73 93

87 83 60

139 91

123 77 30

n

0.4% 99.6%

80.9% 19.1%

27.8% 31.7% 40.4%

37.8% 36.1% 26.1%

60.4% 39.6%

53.5% 33.5% 13.0%

%

T2 Mild disability, alive (n = 230)

Table 1. Baseline characteristics of the five trajectories

0 111

85 26

19 33 59

38 47 26

54 57

55 44 12

n

0.0% 100.0%

76.6% 23.4%

17.1% 29.7% 53.2%

34.2% 42.3% 23.4%

48.6% 51.4%

49.5% 39.6% 10.8%

%

T3 Major disability, alive (n = 111)

1 183

134 50

34 53 97

49 85 50

65 119

73 44 67

n

0.5% 99.5%

72.8% 27.2%

18.5% 28.8% 52.7%

26.6% 46.2% 27.2%

35.3% 64.7%

39.7% 23.9% 36.4%

%

T4 Mild disability, dead (n = 184)

0 184

98 86

19 51 114

79 63 42

92 92

45 51 88

n

0.0% 100.0%

53.3% 46.8%

10.3% 27.7% 62.0%

42.9% 34.2% 22.8%

50.0% 50.0%

24.5% 27.7% 47.8%

%

T5 Major disability, dead (n = 184)

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educated respondents (7+ years) were the most likely to experience no disability (T1) and were the least likely to suffer from major disabilities (T3 and T5), whereas the less educated respondents (none and 1–6 years) were more likely to suffer from mild disabilities (T2 and T4). The majority of the respondents in the mild and/or major disability trajectory clusters (T2–T5) reported that they had been diagnosed with at least two or more chronic diseases and did not have any depressive symptoms at baseline. Notably, however, depressive symptoms increased from 11.9% in T1 to 46.8% in T5 and that almost all of the respondents in each of the disability trajectory clusters (T1–T5) used at least one or more of the social services. Table 2 presents the disability trajectory clusters for the 810 respondents and the frequency of each disability cluster during each of the six data collection waves (1994–2004). Among the surviving respondents, only 23% (101/442) remained free from disability during 1994–2004 and approximately 77% (341/442) experienced some form of disability (ranging from mild to major). Among the nonsurviving respondents, approximately 50% (184/368) suffered a mild disability prior to and/or inclusive of death and a further 50% (184/368) suffered a major disability prior to death. Figure 1 shows the percentage of respondents in each disability trajectory cluster over the six data collection years. This figure highlights an increasing trend in the no disability (T1), mild disability (T2), and major disability (T3) clusters for surviving respondents. In contrast, however, there is a decreasing trend for nonsurviving respondents to be in the mild disability (T4) and major disability (T5) clusters. Figure 2 illustrates the disability trajectories of the mean IADL/ADL scores for all respondents in each trajectory cluster. For nonsurviving

Table 2. Frequency distribution of the five trajectory clusters of disability, 1994–2004 1994 (W1) Trajectory cluster T1—no disability, alive T2—mild disability, alive T3—major disability, alive T4—mild disability, dead T5—major disability, dead Total

1996 (W2)

1998 (W3)

2000 (W4)

n

%

n

%

n

%

n

%

101 230 111 184 184 810

12.5 28.4 13.7 22.7 22.7

101 230 111 132 147 721

14.0 31.9 15.4 18.3 20.4

101 230 111 96 115 653

15.5 35.2 17.0 14.7 17.6

101 230 111 54 75 571

17.7 40.3 19.4 9.5 13.1

Note. T = Trajectory; W = Wave.

2002 (W5) n

%

2004 (W6) n

%

101 19.8 101 22.9 230 45.2 230 52.0 111 21.8 111 25.1 25 4.9 42 8.3 509 442

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60.0%

T2 mild disability, alive

50.0%

Percentage

40.0% T3 major disability, alive

30.0% 20.0%

T1 no disability, alive T5 major disability, dead T4 mild disability, dead

10.0% 0.0%

1994

1996

1998

2000

2002

2004

Year

T1 no disability, alive

T2 mild disability, alive

T3 major disability, alive

T4 mild disability, dead

T5 major disability, dead

Figure 1. Distribution by year for the five disability trajectory clusters.

respondents, individual scores were only available up to the data collection wave prior to their death, since there was no disability score assigned for those who died. This figure clearly indicates that the mean score for

Mean score of IADL and ADL

18 T5. Major disability, dead

16 14 12

T3. Major disability, alive

10 8

T2. Mild disability, alive T4. Mild disability, dead T1. No disability, alive

6 4 2 0 1994

1996

1998

2000

2002

2004

Year T1. No disability, alive

T2. Mild disability, alive

T3. Major disability, alive

T4. Mild disability, dead

T5. Major disability, dead

Figure 2. Mean IADL/ADL scores by year for the five disability trajectory clusters.

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nonsurviving respondents was higher during each data collection year, as compared with the mean scores for surviving respondents in the analogous cluster (i.e., T5 vs. T3 and T4 vs. T2). Using a multinomial model, the odds ratios for disability trajectory clusters were calculated. For each predictor variable (chronic disease(s), depression, and service use), the ORs for disability T2–T5 (compared with the reference T1, “no disability, alive”) are presented in Tables 3 and 4. The Relationships of Trajectories With Health Status For surviving respondents, with hypertension diagnosed at baseline, the ORs were greater than 1 for T2 (mild disability, alive) and T3 (major disability, alive), whereas for surviving respondents with depression diagnosed at baseline, the ORs were greater than 1 for T3 (major disability, alive). In addition, for surviving respondents who were diagnosed with depression during subsequent data collection waves (after baseline), the ORs were greater than 1 for T2 and T3. These results imply that surviving respondents who suffered from hypertension (at baseline) and/or depression (at baseline or as a new occurrence during subsequent years) were at higher risk of developing a including a major disability. Nonsurviving respondents who had been diagnosed with diabetes at baseline had ORs greater than 1 for T4 (mild disability, dead) and T5 (major disability, dead), whereas nonsurviving respondents diagnosed with hypertension and depression at baseline had ORs greater than 1 for Trajectory 5 (major disability, dead) only. Thus, these results suggest that a diagnosis of diabetes, hypertension, and/or depression at baseline increased nonsurviving respondents’ risk of developing a disability prior to death. Notably, however, for nonsurviving respondents who had a diagnosis of arthritis (at either baseline or during subsequent years) or a new diagnosis of gastrointestinal problems during subsequent years, the ORs were less than 1 for T4 and/or T5, suggesting that they were not positively associated with disability prior to death. The Relationships of Trajectories With Social Service Use A greater use of most social services for both surviving and nonsurviving respondents was a strong predictor of disability trajectories, except for the use of recreational services, which seemed to have a negative effect on the development of disabilities. For both surviving and nonsurviving respondents who used recreational service, the odd ratios were lower than 1 for disability T2–T5, suggesting that a greater use of recreational services were associated with a lower risk of developing all levels of disability.

(0.37−1.66) (0.86−10.85) (0.45−2.34) (0.54−2.73) (1.25−5.38) (0.66−3.53)

(0.54−1.70) (0.51−2.08) (0.59−1.78) (0.54−1.72) (0.91−2.88) (1.03−3.32

0.96 1.03 1.03 0.96 1.62 1.85

95% CI

0.79 3.05 1.03 1.21 2.59 1.53

OR





.879 .945 .928 .890 .099

.321

.528 .085 .950 .647

p

1.17 1.18 1.56 1.77 1.91 2.93

1.16 1.64 1.39 1.54 3.06 2.82

OR

(0.60−2.28) (0.53−2.60) (0.81−2.98) (0.91−3.45) (0.97−3.77) (1.39−6.18)

(0.50−2.70) (0.38−7.07) (0.53−3.68) (0.60−3.94) (1.32−7.09) (1.05−7.55)

95% CI

∗∗

.650 .689 .181 .091 .061



∗∗

.734 .510 .503 .368

p

T3 Major disability, alive

0.34 0.51 0.34 0.70 0.61 0.57

0.56 7.35 0.56 2.01 1.69 1.44

OR

(0.17−0.68) (0.20−1.28) (0.18−0.63) (0.35−1.41) (0.32−1.20) (0.29−

(0.26−1.24) (2.04−26.43) (0.23−1.37) (0.87−4.64) (0.79−3.62) (0.62−3.35)

95% CI

.321 .151 .091

∗∗

.152

∗∗

.204 .104 .178 .395

∗∗

p

.152

T4 Mild disability, dead

0.39 0.45 0.50 0.70 0.77 1.98

0.40 6.39 0.85 1.85 2.94 5.73

OR

(0.20−0.77) (0.17−1.19) (0.27−0.94) (0.34−1.43) (0.39−1.53) (0.99−3.96)

(0.18−0.89) (1.75−23.32) (0.35−2.10) (0.79−4.34) (1.36−6.36) (2.40−13.69)

95% CI



p

∗∗



.328 .454 .054

.108

∗∗

∗∗∗

.729 .155

∗∗

T5 Major disability, dead

Note. The reference is T1: no disability, alive. Adjusted odds ratios (ORs) and 95% confidence intervals are presented. ORs adjusted for age, gender, and education. Significant ORs are in bold: ∗∗∗ p < .001; ∗∗ p < .01; ∗ p < .05.

Baseline health (Ref = No) Arthritis (Yes) Diabetes (Yes) GIT problems (Yes) Heart disease (Yes) Hypertension (Yes) Depression (Yes) New occurrence (Ref = No) Arthritis (Yes) Diabetes (Yes) GIT problems (Yes) Heart disease (Yes) Hypertension (Yes) Depression (Yes)

Health status

T2 Mild disability, alive

Table 3. Predictive ability of health status for trajectories of disability

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0.74 1.73 0.97 1.19 1.17 1.61

Recreational services Homemaker-household Meal preparation Personal care Nursing care Physical therapy

0.63–0.87 0.96–3.12 0.55–1.71 0.61–2.32 0.71–1.92 1.03–2.51

95% CI

p



.067 .909 .618 .540

∗∗∗

0.80 2.07 0.76 4.03 1.02 1.93

OR 0.65–0.99 1.04–4.12 0.39–1.48 2.05–7.92 0.59–1.77 1.19–3.13

95% CI

∗∗

.935

∗∗∗

.412





p

T3 Major disability, alive

0.54 0.58 0.88 1.57 1.44 0.82

OR 0.43–0.68 0.30–1.13 0.46–1.67 0.73–3.36 0.82–2.52 0.44–1.52

95% CI

p

.111 .702 .246 .208 .524

∗∗∗

T4 Mild disability, dead

0.42 1.43 0.53 6.06 1.48 1.49

OR

0.31–0.57 0.73–2.84 0.27–1.04 3.04–12.09 0.86–2.57 0.89–2.51

95% CI

p

∗∗∗

.157 .131

.300 .065

∗∗∗

T5 Major disability, dead

Note. The reference is T1: no disability, alive. Adjusted odds ratios (ORs) and 95% confidence intervals are presented. ORs adjusted for age, gender, education, and health conditions. Significant ORs are in bold: ∗∗∗ p < .001; ∗∗ p < .01; ∗ p < .05.

OR

Social service use

T2 Mild disability, alive

Table 4. Predictive ability of social service use for trajectories of disability

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For surviving respondents who used physical therapy services, the ORs were greater than 1 for both T2 (mild disability, alive) and T3 (major disability, alive), whereas for surviving respondents who used homemakerhousehold services and/or personal care services, the ORs were greater than 1 for T3 (major disability, alive) only. These results suggest that the greater use of these social services were significantly associated with some level of disability, and in particular a major disability. Notably, personal care service use by nonsurviving respondents was significantly associated with a major disability prior to death, given that the ORs were greater than 1 for T5 (major disability, dead). DISCUSSION In this prospective cohort study of community-dwelling older Taiwanese people, we identified five trajectories of disability and found that the distribution of these trajectories varied for surviving and nonsurviving respondents. The prevalence of disability trajectory clusters increased substantially for the surviving respondents and decreased for the nonsurviving respondents over the data collection years. This study found that there were increasing trends for disability scores to increase among surviving respondents within each trajectory cluster (i.e., no, mild, major disability clusters) and that chronic diseases such as hypertension, diabetes, and depression are important risk factors for developing disability. Furthermore, this study found that even though a greater use of most social services had a strong association with disability trajectories, a greater use of recreational services appeared to have a protective effect against developing disability. Trajectories of Disability This study defined five disability trajectory clusters, which facilitated the identification of the high variability within these disability trajectories. Heterogeneity was found in the three trajectory clusters for surviving respondents and the two trajectory clusters for nonsurviving respondents, even though the disability process was found to be progressive in each group. These findings are in contrast to those of past researchers (Nusselder et al., 2006; Peres et al., 2005), who found an improvement in recovery from disability, and may be explained by the older participants included in this study cohort, by the participants excluded from this study because they had missed a data collection wave, or because of the nonsurviving participants who died after the first data collection wave may have contributed to survivor bias.

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This study found that among the 369 (45.4%) of nonsurviving respondents who experienced a disability during Wave 1 of the data collection period, 50% of them experienced a major disability prior to death. This percentage is slightly higher than the 41% reported by Nusselder et al. (2006) and may be due to the fact that we decided not to exclude deaths from our analyses because they may have led to an underestimation of the presence of disability, as postulated by Deeg (2005). Impact of Chronic Disease and Depression on Trajectories of Disability The results of this study highlight the possible associations between specific chronic disease and the trajectories of disability. This study found that chronic diseases such as diabetes, hypertension, and depression were significantly associated with the development of disability. These results are consistent with previous studies (Balzi et al., 2010; Deeg, 2005; He & MacGregor, 2007; Lu, Lin, & Kuo, 2009; Stuck et al., 1999) that suggest that the burden of chronic disease (including hypertension) and depression can be important risk factors contributing to increasing levels of disability, especially among older people. Moreover, our findings suggest that diabetes was only found to be a strong predictor of increased disability among the nonsurviving respondents, which is in contrast to the findings of Deeg (2005) who found that diabetes was a strong predictor of disability among surviving respondents. Perhaps this discrepancy may be due to the relatively small number of diabetes cases in this study (baseline: n = 98; new onset: n = 94). Furthermore, since diabetes mellitus and hypertension are two common diseases that often coexist (Grossman & Messerli, 2008) and are responsible for premature death, it could be expected that when they did coexist among older participants in this study they may have contributed to an increased likelihood of developing a disability, as well as increased mortality rates among participants. Similar to evidence in the literature (Deeg, 2005; Mehta et al., 2002), the current study found that depression was an important predictor of developing disability, and especially of developing a major disability prior to death. The current study also found that new occurrences of depression can have a significant impact on the development of both mild and major disabilities in surviving respondents. It could be argued, therefore, that the early and effective management of depressive symptoms in older people could be a valuable way in which to prevent or delay the development of disabilities. In contrast to previous findings (Covinsky, 2006; Peres et al., 2005), this study found that respondents suffering from arthritis (at baseline and/or as a new occurrence) did not experience significant increases in disability prior to death. Once again, selection bias may have impacted on this result

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because only a small number of the respondents actually suffered from arthritis at baseline (n = 148). Another possible explanation for this weak association between arthritis and disability may be that respondents in this study were community dwelling and not institutionalized. Similar to the current study findings that arthritis did not increase disability prior to death, new occurrences of gastrointestinal problems, diagnosed after the baseline data collection wave, were also found to not be associated with an increase in disability prior to death for the nonsurviving respondents. Further research into this anomaly is therefore warranted. Overall, the results of this study highlight the importance of focusing on multiple factors in a disability assessment program and reinforcing the need to prioritize prevention and/or treatment of both chronic disease(s) and depression as an integral component of health care provision. Furthermore, even though the findings of the current study were contrary to the evidence in the literature about the associations between arthritis and diabetes, there is very little doubt that health care providers need to recognize that older people suffering from chronic diseases such as hypertension, diabetes, and arthritis as well as depression are at risk of developing disabilities. Health care providers should also be cognizant of the fact that these multiple factors can vary between individuals and over time, thereby impacting upon their own individual disability pathway. For a better understanding of the disability process and the impact of these multiple factors on an individual level, further research will require more detailed information on the impact of chronic disease(s) and depression, especially for older people. Impact of Social Service Use on Trajectories of Disability Consistent with previous studies (Lennartsson & Silverstein, 2001; Maier & Klumb, 2006), this study found that the high use of recreational services was found to protect against disability. It could be argued, therefore, that older people should be encouraged to attend recreational services and when appropriate should be offered relevant support services (e.g., physical support services) to help reduce the onset of disability. Similar to findings in the literature (Fredman, Droge, & Rabin, 1992; Kemper, 1992; Penning, 2002), this study found that users of homebased social services such as homemaker-household, personal care, and physical support were more likely to be disability. Since the relationship between disability and service use could be reciprocal, based on these findings we cannot conclude a causal relationship between social service use and disability. However, it can be argued that health decline often results in an increased demand for social service use, especially among

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this increasingly vulnerable older group (Broe et al., 2002; Wan, 1987; Wu et al., 2013). Ultimately, therefore, analyzing individual functional activities could be advantageous in determining appropriate social service care needs and in predicting long-term care service use by older people. A limitation of this study is the reliance on the self-reported health status data received by the respondents, even though it is considered an accepted means of collecting chronic disease data (Newell, Girgis, Sanson-Fisher, & Savolainen, 1999). It is possible, therefore, that participants in this study may have reported being diagnosed with a chronic disease during one interview wave, but may have been inconsistent in their reporting during later waves. It is envisaged, however, that since respondents were asked to base their responses on what they were told by their doctors, any such bias should have been minimal. Furthermore, other health status recall bias relating to disability and depression would have been minimized by using valid tools for assessing disability (ADL and IADL scales) and depressive symptoms (SPES) (Chiu et al., 1997) and by measuring changes in disability by classifying different levels and pathways of disability. A second limitation of this study relates to the study population, which focused on community-living older Taiwanese people; thus, it may not be representative of older Taiwanese people living within hospitals and/or other institutions. This remains an area requiring further future research. CONCLUSION In conclusion, these results confirm the notion that disability is a dynamic process in aging, characterized by different trajectories ranging from no disability to mild disability to major disability among both the surviving and nonsurviving respondents. A future focus on the effects of chronic diseases such as diabetes and hypertension, as well as the effects of depression on the progress of disability, is strongly recommended by the current research. This research also provides important information to support the development of more targeted, cost-effective health interventions to be used in combination with home-based social services, which could help to slow down the process of functional health decline, especially in an aging population. FUNDING The research presented in this article was supported by a grant from the National Research Institute of Health (NHRI-GT-EX89P903P, NHRIDD01-86IX-GR-602S) and Department of Health (DOH-HR-222 DOH), Taiwan.

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Trajectories of disability and their relationship with health status and social service use.

BACKGROUND/STUDY CONTEXT: This longitudinal study was conducted between 1994 and 2004 in a cohort of southern Taiwan community-living older residents...
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